![]() Except, which introduced a misspelling detector and corrector system for five Ethiopian languages in a single system, no one has developed a misspelling detector and corrector system for more than three Ethiopian languages in a single system. In this study, the researchers have used a dictionary-based model for spelling error detector and corrector to develop, build, and test an end-to-end system for six Ethiopian languages. However, no one has developed such systems for more than five Ethiopian languages. ĭifferent misspelling detection and correction systems have been developed by various professionals in the field for both foreign and Ethiopian languages. However, a lack of appropriate datasets and good word embeddings have made it difficult to develop different systems that are reliable enough. Despite this, Ethiopian languages remain among the world’s “low-resource” languages, lacking the tools and resources required for NLPA and other techno-linguistic activities. However, “low-resource” languages, primarily African languages, lack such tools and resources. Linguistic resources are vital in the development of natural language processing applications (NLPA). This involves the development of an accurate and reliable misspelling detection and correction system that can be integrated with word processing tools. Spelling errors on printed sheets are still pretty common even after this method. A devoted individual reads through written papers in various printing presses, discovers misspelled words, and corrects them. The vast majority of people who type Amharic, Afan Oromo, Tigrinya, Hadiyyisa, Kambatissa, and Awngi make mistakes without realizing it. It is fairly common to find numerous spelling errors in typed writings in various languages. Furthermore, recommendations have been provided for future researchers. The system outperforms an f-measure of 89.57%, 87.57%, 88.31%, 86.83%, 81.83%, and 87.59% for Amharic, Afan Oromo, Tigrinya, Hadiyyisa, Kambatissa, and Awngi languages respectively. ![]() Finally, precision, recall, and f-measures for each language have been computed following a successful evaluation of the model. Similarly, the user(s) can improve their skills in the selected languages accordingly. Here, the users can save time and perform their tasks easily. That means if the user(s) is (are) working on the Amharic language and then he/she/they can change the language she/he/they prefer(s) without shifting to another graphical user interface (GUI). In this model, the users can perform all spelling-related issues within a single system (all-in-one). The corpora used were gathered from a variety of sources, including economic, political, social, and related publications, newspapers, and magazines. ![]() In addition, the proposed model is evaluated using dictionary-based data sets for all languages. The major characteristics of the proposed model can be outlined by presenting suggestions for detected flaws and automatically correcting them utilizing the first suggestion. A dictionary-based methodology is used to detect and rectify various forms of misspelling-related issues. ![]() However, an effective and all-in-one typo detector and corrector system for Ethiopian languages have yet to be developed. For some of these languages, there have been few works on typo detection and correction systems. In this paper, a misspelling detection and correction system was developed for Ethiopian languages (Amharic, Afan Oromo, Tigrinya, Hadiyyisa, Kambatissa, and Awngi). ![]()
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